基于k值优化VMD的滚动轴承故障诊断方法
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摘要
针对旋转机械中滚动轴承早期信噪比较低的故障特征提取困难问题,提出了一种基于能量的变分模式分解(variational mode decomposition,简称VMD)模态数k优化选取方法,用以提取滚动轴承早期故障特征,同时避免了信号分解过分或不足。首先,对振动信号进行VMD预分解,分别在不同k值条件下计算分量信号能量与原始信号总能量;其次,根据基于能量的模态数k选取准则,确定最佳模态数值对信号进行VMD分解;最后,通过峭度准则选择分量进行信号重构,对其进行包络分析,提取故障特征频率。将该方法运用到实际故障信号中,有效提取出滚动轴承内圈微弱故障特征,实现了早期故障特征判别,具有一定的应用价值和实际意义。
引文
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[17]苏文胜,王奉涛,张志新,等.EMD降噪和谱峭度法在滚动轴承早期故障诊断中的应用[J].振动与冲击,2010,29(3):18-21.Su Wensheng,Wang Fengtao,Zhang Zhixin,et al.Application of EMD denoising and spectral kurtosis in early fault diagnosis of rolling element bearings[J].Journal of Vibration and Shock,2010,29(3):18-21.
[18]Antoni J.The spectral kurtosis:a useful tool for characterising non-stationary signals[J].Mechanical Systems&Signal Processing,2006,20(2):282-307.
[2]郭红.内外膜独立供油径推联合浮环轴承性能分析与实验研究[D].上海:上海交通大学,2009.
[3]何正嘉,黄昭毅.机械故障诊断案例选编[M].西安:西安交通大学出版社,1991:14-18.
[4]Wei Guo,Peter W T.A novel signal compression method based on optimal ensemble empirical mode decomposition for bearing vibration signals[J].Journal of Sound and Vibration,2013,332(2):423-441.
[5]Cong Feiyun,Chen Jin,Dong Guangming.Vibration model of rolling element bearings in a rotor-bearing system for fault diagnosis[J].Journal of Sound and Vibration,2013,332(8):2081-2097.
[6]王奉涛,陈守海,闫达文,等.基于流形-奇异值熵的滚动轴承故障特征提取[J].振动、测试与诊断,2016,36(2):288-294.Wang Fengtao,Chen Shouhai,Yan Dawen,et al.Fault feature extraction method for rolling bearing based on manifold and singular values entropy[J].Journal of Vibration,Measurement&Diagnosis,2016,36(2):288-294.(in Chinese)
[7]Huang N E,Shen Zheng,Long S R.A new view of nonlinear water waves:the Hilbert spectrum[J].Annual Review of Fluid Mechanics,2003,31(1):417-457.
[8]Yang Yu,Cheng Junsheng,Zhang Kang.An ensemble local means decomposition method and its application to local rub-impact fault diagnosis of the rotor systems[J].Measurement,2012,45(3):561-570.
[9]任达千.基于局域均值分解的旋转机械故障特征提取方法及系统研究[D].杭州:浙江大学,2008.
[10]程军圣,郑近德,杨宇.一种新的非平稳信号分析方法-局部特征尺度分解法[J].振动工程学报,2012,25(2):215-220.Cheng Junsheng,Zheng Jingde,Yang Yu.A nonstationary signal analysis approach--the local characteristic-scale decomposition method[J].Journal of Vibration Engineering,2012,25(2):215-220.(in Chinese)
[11]Rilling G,Flandrin P.The influence of sampling on the empirical mode decomposition[C]∥IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.Honolulu,HI,USA:[s.n.],2006.
[12]Dragomiretskiy K,Zosso D.Variational mode decomposition[J].IEEE Transactions on Signal Processing,2014,62(3):531-544.
[13]马增强,李亚超,刘政,等.基于变分模态分解和Teager能量算子的滚动轴承故障特征提取[J].振动与冲击,2016,35(13):134-139.Ma Zhenqiang,Li Yachao,Liu Zhen,et al.Rolling bearings′fault feature extraction based on variational mode decomposition and Teager energy operator[J].Journal of Vibration and Shock,2016,35(13):134-139.(in Chinese)
[14]郑小霞,周国旺,任浩翰,等.基于变分模态分解的风机滚动轴承早期故障诊断[J].轴承,2016(7):48-53.Zheng Xiaoxia,Zhou Guowang,Ren Haohan,et al.Incipient fault diagnosis for rolling bearings used in wind turbine based on variational mode decomposition[J].Bearing,2016(7):48-53.(in Chinese)
[15]Dwyer R.Detection of non-Gaussian signals by frequency domain Kurtosis estimateon[C]∥IEEE International Conference on Acoustics,Speech,and Signal Processing.Boston,Massachusetts,USA:[s.n.],1983.
[16]Antoni J.Fast computation of the kurtogram for the detection of transient faults[J].Mechanical Systems and Signal Processing,2007,21(1):108-124.
[17]苏文胜,王奉涛,张志新,等.EMD降噪和谱峭度法在滚动轴承早期故障诊断中的应用[J].振动与冲击,2010,29(3):18-21.Su Wensheng,Wang Fengtao,Zhang Zhixin,et al.Application of EMD denoising and spectral kurtosis in early fault diagnosis of rolling element bearings[J].Journal of Vibration and Shock,2010,29(3):18-21.
[18]Antoni J.The spectral kurtosis:a useful tool for characterising non-stationary signals[J].Mechanical Systems&Signal Processing,2006,20(2):282-307.